✳flâneur — a map of the web's best reading
The Crux of Every AI System: Evaluations | WHOOP Engineering
engineering.prod.whoop.com · 1,776 words · saved by 1 readers
How we built a framework from scratch to ensure quality and stability for WHOOP AI.
Across the industry, we see AI features shipped on hope alone. At WHOOP, we ship with data and security in mind. In order to support over 500 unique agents, we built an evaluation framework that treats LLMs like the statistical, noisy systems they are. Here's exactly how it works. The Problem We've built AI Studio to enable anyone at WHOOP to develop and interact with our homegrown Agents, resulting in an explosion of more than 500 of them across virtually every screen in the app. But as we reduced the friction to build Agents, the new bottleneck became testing them. Manual dogfooding turns
Explore this link on the map →related reading
- Demystifying evals for AI agents \ Anthropicanthropic.com
- Your AI Product Needs Evals – Hamel's Blog - Hamel Husainhamel.dev
- Agent Evaluation: A Detailed Guidecameronrwolfe.substack.com
- Your Evals Will Break and You Won't See It Coming - Lun Wangwanglun1996.github.io
- Building an LLM evaluation framework: best practices | Datadogdatadoghq.com
- The bitter lesson of LLM evalsparsed.com
- How to Eval AI Agents — The 2026 Guidehowtoeval.com
- A pragmatic guide to LLM evals for devsnewsletter.pragmaticengineer.com
- Agent Observability and Tracingarize.com
- Building Effective AI Agents \ Anthropicanthropic.com
- LLM evaluation: a beginner's guideevidentlyai.com
- Building Effective AI Agents \ Anthropicanthropic.com